Navigation based on radar-cued visual imaging

ABSTRACT

A navigation system for a vehicle may include at least one image capture device configured to acquire a plurality of images of an environment of a vehicle and a radar sensor to detect an object in the environment of the vehicle and to provide and output including range information indicative of at least one of a range or range rate between the vehicle and the object. The system may also include at least one processing device programmed to: receive the plurality of images from the at least one image capture device; receive the output from the radar sensor; determine, for each of a plurality of image segments in a first image, from among the plurality of images, and corresponding image segments in a second image, from among the plurality of images, an indicator of optical flow; use range information determined based on the output of the radar sensor together with the indicators of optical flow determined for each of the plurality of image segments in the first image and the corresponding image segments in the second image to calculate for each of a plurality of imaged regions at least one value indicative of a focus of expansion; identify a target object region, including at least a subset of the plurality of imaged regions that share a substantially similar focus of expansion; and cause a system response based on the identified target object region.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 61/942,088, filed on Feb. 20, 2014. The foregoingapplication is incorporated herein by reference in its entirety.

BACKGROUND I. Technical Field

The present disclosure relates generally to vehicle navigation and, morespecifically, to systems and methods that fuse radar and visualinformation to navigate relative to detected objects.

II. Background Information

Various sensors may be employed, whether in a driver assist system or anautonomous vehicle, in order to aid in navigation of the vehicle. Suchsensors may include radar units and cameras, among others. These sensorsmay collect information from the environment of a vehicle and use thecollected information to make navigational decisions relative to variousobjects, hazards, etc. present in the environment of the vehicle. Forexample, the sensors may collect information associated with objects ina roadway, other vehicles, light poles, guard rails, pedestrians, amongothers. Information collected from the onboard sensor systems may enabledriver assist or autonomous driving systems to recognize objects in avehicle's environment and take appropriate action to reduce or minimizethe risk of collisions.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras and/or radar to provide autonomous vehicle navigationfeatures.

Consistent with a disclosed embodiment, a navigation system for avehicle may include at least one image capture device configured toacquire a plurality of images of an environment of a vehicle and a radarsensor to detect an object in the environment of the vehicle and toprovide and output indicative of a range and/or range rate between thevehicle and the object. The system may also include at least oneprocessing device programmed to: receive the plurality of images fromthe at least one image capture device; receive the output from the radarsensor; determine, for each of a plurality of image segments in a firstimage, from among the plurality of images, and corresponding imagesegments in a second image, from among the plurality of images, anindicator of optical flow; determine a value indicative of an expectedoptical inflation associated with the object in the plurality of imagesbased at least upon range and/or range rate information determined fromthe output of the radar sensor; determine one or more areas of thesecond image where the value indicative of an expected optical inflationassociated with the object substantially matches the indicator ofoptical flow; identify a target object region, including at least asubset of the determined one or more areas of the second image; andcause a system response based on the identified target object region.

The disclosed embodiments may also include a vehicle comprising anavigation system. The navigation system may include at least one imagecapture device configured to acquire a plurality of images of anenvironment of a vehicle and a radar sensor to detect an object in theenvironment of the vehicle and to provide and output indicative of arange and/or range rate between the vehicle and the object. The systemmay also include at least one processing device programmed to: receivethe plurality of images from the at least one image capture device;receive the output from the radar sensor; determine, for each of aplurality of image segments in a first image, from among the pluralityof images, and corresponding image segments in a second image, fromamong the plurality of images, an indicator of optical flow; determine avalue indicative of an expected optical inflation associated with theobject in the plurality of images based at least upon range and/or rangerate information determined from the output of the radar sensor;determine one or more areas of the second image where the valueindicative of an expected optical inflation associated with the objectsubstantially matches the indicator of optical flow; identify a targetobject region, including at least a subset of the determined one or moreareas of the second image; and cause a system response based on theidentified target object region.

The disclosed embodiments may also include a method for navigating avehicle based on radar detection and visual image data. The method mayinclude acquiring a plurality of images of an environment of thevehicle; detecting an object in the environment of the vehicle using aradar sensor and providing an output of the radar sensor indicative of arange and/or range rate between the object and the vehicle; receiving atone or more processing devices the plurality of images from the at leastone image capture device and the output from the radar sensor;determining an indicator of optical flow, for each of a plurality ofimage segments in a first image, from among the plurality of images, andcorresponding image segments in a second image, from among the pluralityof images; identifying a target object region having a substantiallycommon focus of expansion, based on range information provided by theradar sensor and the determined indicators of optical flow; and causinga system response based on the identified target object region.

The disclosed embodiments may include a navigation system for a vehicle.The system may include at least one image capture device configured toacquire a plurality of images of an environment of a vehicle; a radarsensor to detect an object in the environment of the vehicle and toprovide and output indicative of a range and/or range rate between thevehicle and the object; and at least one processing device. The at leastone processing device may be programmed to receive the plurality ofimages from the at least one image capture device; receive the outputfrom the radar sensor; determine, for each of a plurality of imagesegments in a first image, from among the plurality of images, andcorresponding image segments in a second image, from among the pluralityof images, an indicator of optical flow; identify a target object regionhaving a substantially common focus of expansion, based on range and/orrange rate information provided by the radar sensor and the determinedindicators of optical flow; and cause a system response based on theidentified target object region.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 9A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 9B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 9A consistent with the disclosed embodiments.

FIG. 9C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 9D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 9E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 10 is a diagrammatic representation of an environment in which avehicle may travel.

FIG. 11A represents an image of an object within an environment of thevehicle.

FIG. 11B represents another image of the object within an environment ofthe vehicle, from which optical flow may be determined relative to theimage of FIG. 11A.

FIG. 12A represents a horizontal optical flow map relative to the objectof FIG. 11A.

FIG. 12B represents a vertical optical flow map relative to the objectof FIG. 1A.

FIG. 13 represents a target object region derived from optical flowinformation and radar range information according to exemplary disclosedembodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, and a user interface 170. Processingunit 110 may include one or more processing devices. In someembodiments, processing unit 110 may include an applications processor180, an image processor 190, or any other suitable processing device.Similarly, image acquisition unit 120 may include any number of imageacquisition devices and components depending on the requirements of aparticular application. In some embodiments, image acquisition unit 120may include one or more image capture devices (e.g., cameras), such asimage capture device 122, image capture device 124, and image capturedevice 126. System 100 may also include a data interface 128communicatively connecting processing device 110 to image acquisitiondevice 120. For example, data interface 128 may include any wired and/orwireless link or links for transmitting image data acquired by imageaccusation device 120 to processing unit 110.

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), graphicsprocessors, a central processing unit (CPU), support circuits, digitalsignal processors, integrated circuits, memory, or any other types ofdevices suitable for running applications and for image processing andanalysis. In some embodiments, applications processor 180 and/or imageprocessor 190 may include any type of single or multi-core processor,mobile device microcontroller, central processing unit, etc. Variousprocessing devices may be used, including, for example, processorsavailable from manufacturers such as Intel®, AMD®, etc. and may includevarious architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. In other embodiments, configuring a processing device mayinclude storing executable instructions on a memory that is accessibleto the processing device during operation. For example, the processingdevice may access the memory to obtain and execute the storedinstructions during operation.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The support circuits maybe any number of circuits generally well known in the art, includingcache, power supply, clock and input-output circuits. The memory maystore software that, when executed by the processor, controls theoperation of the system. The memory may include databases and imageprocessing software. The memory may comprise any number of random accessmemories, read only memories, flash memories, disk drives, opticalstorage, tape storage, removable storage and other types of storage. Inone instance, the memory may be separate from the processing unit 110.In another instance, the memory may be integrated into the processingunit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware. The memory units may include random access memory, read onlymemory, flash memory, disk drives, optical storage, tape storage,removable storage and/or any other types of storage. In someembodiments, memory units 140, 150 may be separate from the applicationsprocessor 180 and/or image processor 190. In other embodiments, thesememory units may be integrated into applications processor 180 and/orimage processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.).

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope. In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. The disclosed embodiments are not limited to anyparticular configuration of image capture devices 122, 124, and 126,camera mount 370, and glare shield 380. FIG. 3C is an illustration ofcamera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122-126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122-126 may be positioned behind rearviewmirror 310 and positioned substantially side-by-side (e.g., 6 cm apart).Further, in some embodiments, as discussed above, one or more of imagecapture devices 122-126 may be mounted behind glare shield 380 that isflush with the windshield of vehicle 200. Such shielding may act tominimize the impact of any reflections from inside the car on imagecapture devices 122-126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122-126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402-408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto application processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar) to perform the monocular image analysis. As described inconnection with FIGS. 5A-5D below, monocular image analysis module 402may include instructions for detecting a set of features within the setof images, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, and any otherfeature associated with an environment of a vehicle. Based on theanalysis, system 100 (e.g., via processing unit 110) may cause one ormore navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappearing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, Processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560-564, processing unit 110 mayidentify traffic lights appearing within the set of captured images andanalyze junction geometry information. Based on the identification andthe analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(i), z_(i))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(i)/z_(i)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift,braking, a change in acceleration, and the like. Processing unit 110 maycause the one or more navigational responses based on the analysisperformed at step 720 and the techniques as described above inconnection with FIG. 4. Processing unit 110 may also use data derivedfrom execution of velocity and acceleration module 406 to cause the oneor more navigational responses. In some embodiments, processing unit 110may cause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Radar-Cued Visual System for Vehicle Navigation

Radar systems offer the potential for detecting objects in anenvironment surrounding a vehicle, even in conditions where visibilitymay be poor. Therefore, there is an interest in including radar sensorsystems on vehicles for purposes of aiding in navigation. For example,driver-assist systems, collision warning/avoidance systems, autonomousvehicles, and semi-autonomous vehicles may all benefit from thepotential target object detection capabilities that radar systems offer.

While radar sensors may provide accurate ranging information, they alsocan exhibit certain deficiencies. First, radar sensors may generatefrequent “ghost” detections, which may cause false positives in avehicle navigation or collision avoidance system. Such unwanted ghostdetections may result, for example, when objects are present in thevicinity of the vehicle, and those objects exhibit good radarreflectivity, but pose little or no threat to the vehicle. Such objectsmay include those with non-upright profiles or insignificant size.Manhole covers, soda cans, small debris, etc. may result in radar ghostdetections. In some instances, ghost detections may occur even when noobject is present.

Second, radar sensors may offer angular resolution and angular accuracycharacteristics that may be unsuitable for certain vehicle applications.For example, forward collision warning systems and vehicle navigationsystems (either autonomous or driver assist) may require accurateinformation regarding the angular location of a target object relativeto the vehicle or a path of travel of the vehicle.

To enhance the reliability and usability of radar-based sensor systems,such radar systems may be deployed on a vehicle in combination with animage sensor system including one or more image acquisition devices. Theradar and image systems may be used in tandem to reduce or eliminate theoccurrence of ghost detections by the radar system and to accuratelydetermine one or more of the angular position of a target objectrelative to the vehicle and the solid viewing angle that a target objectoccupies within the field of view of an image acquisition device. From acombination of range information from the radar, as well as spatiallocation information derived from the image system, performance ofcollision warning systems, driver assist systems, and other types ofvehicle navigation systems may be enhanced.

FIG. 8 provides a diagrammatic representation of a system 100 along withall of the same parts and features associated with system 100 asdescribed above relative to FIGS. 1, 2A, 2B, 2C, 2D, 2E, and others. Inthe embodiment shown in FIG. 8, a radar unit 801, including one or moreradar sensors 802, is included in system 100. Radar unit 801 may providean output either directly or indirectly to application processor 180 (orany other suitable logic based device) via, for example, data interface128.

In some embodiments, radar unit 801 may be combined with an imageacquisition unit 120 that includes a single image capture device ormultiple image capture devices. In some cases, radar unit 801 may beincluded on a vehicle equipped with two image capture devices (e.g.,image capture device 122 and image capture device 124) as shown in FIGS.9A, 9B, and 9C. In other embodiments, radar unit 801 may be included ona vehicle equipped with three image capture devices (e.g., image capturedevice 122, image capture device 124, and image capture device 126) asshown in FIGS. 8, 9D, and 9E.

Radar unit 801 may be located at any suitable location relative tovehicle 200. In some cases, as shown in FIGS. 9A-9E, radar unit 801 maybe located on the roof of the vehicle. In other cases, radar unit 801may be located in front of or behind the windshield, in the grill, in oron the forward bumper (in or on the rear bumper, in some rear facingcases), in or on a side panel (e.g., for blind spot detection), or atany other viable location on vehicle 200.

Radar unit 801 may include any suitable type of radar useful inidentifying the presence of target objects in a vehicle navigationapplication. For example, in some embodiments, radar unit 801 mayinclude mechanically scanning radar. In other embodiments,electronically scanning radar may be used that reduce or eliminatemoving parts from radar unit 801. Any suitable frequencies or frequencyranges may be employed. For example, radar units operating atfrequencies of 79 GHz, 77 GHz, 25 GHz, or 24 GHz may be used. Such unitsmay offer various detection ranges along with various scan angles. Insome embodiments, radar unit 801 may have a detection range of 100 m,200 m, 250 m, or more and may offer scan angles of up to 360 degrees.

In the disclosed embodiments, radar unit 801 may provide informationrelating to one or multiple target objects in an environment of vehicle200. Such information may include, for example, an indication of thepresence of one or more target objects in the vicinity of vehicle 200and an indication of a distance (range) to the one or more targetobjects. In some cases, additional information may be available fromradar unit 801, such as a radar range rate (e.g., a rate that a targetobject is moving toward or away relative to vehicle 200). The radarrange and/or radar range rate information provided from radar unit 801may serve as an indicator of a time to contact between vehicle 200 and atarget object.

During operation, radar unit 801 may provide a radar range, radar angle,and/or a radar range rate relative to a target object either directly orindirectly to application processor 180. Using this information,application processor 180 may determine a time to contact betweenvehicle 200 and a particular target object.

In addition to radar information, application processor 180 and/or imageprocessor 190 may receive image data captured by one or more of imagecapture device 122, 124, or 126. This image data may be received in theform of a series of image frames captured at predetermined timeintervals.

Using the received radar information together with the plurality ofimages, processing unit 110 may determine whether a particular radardetection correlates to a real world target object. If a target objectis validated, processing unit 110 may identify physical attributes ofthe target object such as edges of the object, height of the objectrelative to a road surface or other surface, and angular location of thetarget object relative to vehicle 200.

More specifically, a process for validating and locating a target objectmay include the identification of a rough region of interest (ROI) inone or more of the received plurality of images from the imageacquisition unit 120. For example, based on the angle and rangeassociated with a particular radar detection, and knowing values ofvarious parameters associated with the image acquisition device thatsupplied a certain image (e.g., the camera/radar calibration values,camera focal length, and height of the camera above the road),processing unit 110 may determine what region of an acquired image islikely to include a target object associated with a particular radardetection. Such a rough region of interest in the acquired image may bedesignated by left, right, bottom, and top boundaries of a region of theacquired image where the target object may be found.

Next, the optical flow between the rough region of interest in theacquired image and a corresponding region of a second acquired image maybe determined. Conceptually, the optical flow between at least a pair ofacquired images may be associated with the changing angular position ofcorresponding points in the images. As an example, as a vehicle travelsdirectly toward a target object on a roadway, the target object willappear to the driver as expanding radially in all directions as thevehicle moves closer to the target object. Similarly, in images capturedover time from an image captured device located in or on a vehicle, atarget object captured in the images will appear to expand radially inall directions relative to images captured earlier in a sequence ofimages. The non-moving point directly in front of the vehicle at thecenter of the horizon, from which the objects in a sequence of imagescaptured from a moving vehicle appear to emanate, is referred to as thefocus of expansion.

Regarding objects in motion, if the target object is moving relative tothe stationary world, the FOE will reflect the relative motion betweenthe host vehicle and the target object. For example, if a vehicle isapproaching from the right (at an intersection for example) the FOE willbe to the right of the image. If the vehicle and the target object areon a collision course, the FOE will be on the target object. Because theFOE depends on the motion of the target, the FOE may be useful inisolating the target from its surroundings within one or more images ofa scene.

In practice, the optical flow between two images and, more specifically,between a particular feature point within a region of interest in afirst image and a corresponding feature point in a region of interest ina second image may be determined in a number of ways. As a first step,pairs of corresponding feature points from the two images may bedetermined. In one technique, a pair of corresponding feature points maybe determined by defining a patch around a particular feature point inthe region of interest of the first image (or about every pixel in theregion of interest of the first image). Then, every defined patch fromthe first image may be compared with patches from the second imagewithin a predetermined search area (e.g., within a predetermined orselectable range of pixels) relative to a selected patch from the firstimage. From every patch comparison, a correlation score may bedetermined based on the similarity of the compared patches. The patchfrom the second image that provides the highest correlation score withthe patch from the first image containing the feature point of interestdetermines the location of that feature point in the second image.

Another technique for determining pairs of corresponding feature pointsfrom the two images involves matching or registration based on detectedfeature points. For example, certain feature points (e.g., corners ofobjects or other well-defined points in the image data) may be detectedin the first image through image analysis. Similar feature points mayalso be detected in a second image. Patches may be defined around thedetected feature points from both the first and second images. Anynumber of feature points may be detected within a region of interest(e.g., a region that includes 10 feature points, 20 feature points, 50feature points, or more). A correlation score may be calculated for eachpatch defined for the first image by comparing each individual patch ofthe first image with each patch of the second image. The pair of patches(one from the first image and one from the second image) with thehighest correlation score indicates the location of a particular,detected feature point in both the first and second images.

Once the locations are known for a set of feature points (or patches orother suitable image segment) in the first image and the correspondingset of feature points (or patches or any other suitable image segment)in the second image, the optical flow between the images may bedetermined. For example, for each corresponding pair of feature points(patches or image segments) in the pair of images, the motion thatresulted in movement of the feature points from their location in thefirst image to their location in the second image may be determined.This motion represents the optical flow between the corresponding pointsof the pair of images. The optical flow inside the region of interestdefined for the first image may be represented as a set of {(x,y,u,v)i}quadruples representing image positions (x,y)i and the 2D flow (u,v)imeasured at those positions.

If a rolling shutter is employed on one or more of the image capturedevices, an observed vertical motion in the image may indicate or resultfrom the fact that the two points were imaged at a slightly differenttime. If the rolling shutter scans from top to bottom an upward flow ofN lines, for example, means that the time between imaging is less than aframe (by N line scan times). To get more accurate estimates of theoptical flow, the flow vectors can be corrected by extending in thevector direction by frameTime/(frameTime−N*linetime).

Next, an expected optical inflation value (ds) may be determined basedon information obtained from radar unit 801. For example, using theradar range to the target object, radar range rate between the targetobject and vehicle, and the time between captured images (dt), theexpected optical inflation value in the region of interest may berepresented as:

Optical inflation (ds)=dt/TTC=dt*(range rate)/range;

where TTC is the time to contact between the target object and thevehicle.

Using the expected optical inflation value and the determined opticalflow information, the target object may be isolated within the region ofinterest. For example, using as input the set of {(x,y,u,v)i} quadruplesalong with the optical inflation (ds) expectation, a consensus set ofpixels {(x,y)} may be determined where the optical flow substantiallymatches the expected optical inflation. This consensus set of pixels(image segments, points, or other portions of the region of interest inthe plurality of images) may represent a target object region in theplurality of images occupied by the target object. The points, pixels,image segments, etc. associated with this target object region may sharea common focus of expansion, as opposed to other image areas within theregion of interest that do not correspond to the target object. Forthose other image areas (e.g., image portions associated with the roador background objects), the determined optical flow for those areas willlikely not match the expected optical inflation determined based on thetarget object range and range rate, because the background objects androad (aside from the portion of the road located at the same range asthe target object) will have ranges different from the target object.

Regarding the focus of expansion (FOE) and its relationship to the TTCor time to contact, all stationary points in a captured image will sharethe same true FOE, which corresponds to the direction that vehicle 200is traveling. For an upright object, if a TTC to that object isavailable (e.g., based on the output of radar unit 801), the estimatedFOE for every point in the object's image may be derived based on theoptical flow (i.e., the points' motion in the image). If the TTC forevery point in an image was available, and every point in the imagecould be tracked from frame to frame, then the same FOE may be derivedfor all points in the image. In practice, however, a TTC may beavailable only for a single radar-detected object or a group ofradar-detected objects and not for every point associated with aparticular image. Deriving the FOE based on the TTC value for a singledetected object may yield the same FOE for all imaged points associatedwith that object (assuming the TTC is valid for all points of theobject). On the other hand, using the TTC associated with a targetobject to derive and FOE associated with a background object or aforeground object at a range different from the detected target objectmay result in an FOE different from the one derived for the targetobject. These differences in derived FOE values may be used to isolate atarget object within an image to both confirm that a radar detectioncorresponds to an actual object and to determine the size, shape, andspatial orientation of the target object relative to vehicle 200.

Examining the process for isolating the target object in the imageframes in further detail, when approaching an upright target object in aroadway, the optical flow of a pixel/patch in the image can be describedusing a model as a function of the position in the image, TTC, and theFOE. The radar may detect an object that moves from distance Z₁ in frame1 to distance Z₂ in frame 2. A feature on the target object havingcoordinates (x₁, y₁) may be detected in frame 1 and tracked tocoordinates (x₂, y₂) in frame 2. The point may be assumed as lying at aconstant lateral distance X from the optical axis and at a constantheight Y above the road. Using a pinhole camera model, the motion (Δx,Δy) of the feature point in the image may be given by:

x ₁ =x ₀ +X·f/Z ₁ ,y ₁ =y ₀−(cH−Y)·f/Z ₁

x ₂ =x ₀ +X·f/Z ₂ ,y ₂ =y ₀−(cH−Y)·f/Z ₂

⇒Δx≡x ₂ −x ₁ =X·f·(1/Z ₂−1/Z ₁)=(x ₁ −x ₀)·Z ₁·(1/Z ₂−1/Z ₁)=(x ₁ −x₀)·(Z ₁ −Z ₂)/Z ₂

⇒Δy≡y ₂ −y ₁=−(cH−Y)·f·(1/Z ₂−1/Z ₁)=(y ₁ −y ₀)·Z ₁·(1/Z ₂−1/Z ₁)=(y ₁−y ₀)·(Z ₁ −Z ₂)/Z ₂

where the camera's focal length is given by f and its height by cH. TheFOE is unknown and given by (x₀, y₀).

The motion equations can therefore be parametrized as follows:

Δx=S·(x−x ₀)

Δy=S·(y−y ₀)

where the scale S=(Z₁−Z₂)/Z₂ describes the expansion in the image of theobject relative to the FOE. The time to contact (TTC) with the object,assuming that the relative speed between the target object and thevehicle remains constant, can be derived from the scale S as follows:

S≡−ΔZ/Z

⇒TTC=−Z/V _(rel) =−Z·Δt/ΔZ=Δt/S

In practice, the consensus set of feature points may be described by thelargest union of points whose observed motion can be described by acertain fixed FOE, given the TTC obtained from the radar target. Thesize of this consensus set will define the probability that there is anactual upright object with a TTC as obtained from the radar targetwithin the region of interest as defined by the radar target.

Any suitable approach may be used for detecting the set of pixels in theimage that correspond to the expected TTC, as obtained from the radartarget. In one embodiment, an optimization technique may be used, suchthat given a set of N feature points {(x_(i), y_(i))}_(i=1) ^(N),observed motions {(Δx_(i), Δy_(i))}_(i=1) ^(N) and radar scale (minusthe range divided by the range-rate), we can derive the estimated FOE({circumflex over (x)}₀, ŷ₀) that best describes the observed motion(i.e. the model that predicts motions (

_(i),

_(i)) that are optimal in a sense that they are ‘closest’ to theobserved motions). The number of points whose observed motion vectorsare sufficiently close (euclidian distance measure) to the expectedmotion vectors, will define the consensus set.

In another embodiment, a Hough transform approach may be used. In thisapproach, for each tracked point i, the FOE {x₀(S, Δx_(i), x_(i)), y₀(S,Δy_(i), y_(i))} may be derived as a function of the point's position,measured translation in the image, and the expected TTC or scale. Atwo-dimensional histogram (Hough transformation) may be constructed bydiscretizing the obtained FOE. Then, the value in the histogram thatobtained the highest number of votes may be determined in order todefine the consensus set of points that contributed to that value.

As an output of the target identification process based on the radarinformation together with the visual image information, a target objectregion may be identified in the images. This target object region may bedelineated by an image bounding box (or other shape) encompassing theconsensus set {(x,y)} of pixels that comply with the expected opticalinflation and share a common FOE in agreement with a TTC determinedbased on the detected radar information. Using this target objectregion, the edges of a physical target object may be identified andlocated relative to vehicle 200. Further, the height, shape, width orany other dimensional characteristics of the target object may bedetermined. Combining the target object region in an acquired image withknown parameters of the image acquisition device (e.g., the focallength, height above the road, orientation relative to vehicle 200,etc.) may enable determination of the angular orientation of the targetobject relative to vehicle 200.

Such information may be helpful in determining whether the target objectresides in a path of vehicle 200. For example, if based on the locationin a certain image of the target object region, processing unit 110determines that the target object falls within a travel path of vehicle200, processing unit 200 may cause any of a number of system responses.In some embodiments the system response may include a navigationresponse, such as braking, changing steering direction, etc. Ifprocessing unit 110 determines that allowing vehicle 200 to continuealong the current travel direction may result in a collision with thetarget object, then processing unit may warn a driver of vehicle 200,automatically change a steering direction, automatically apply thevehicle's brakes, etc. For example, processing unit 110 may provide anotice to the driver of the vehicle (e.g., an audible sound or visualindicator via user interface 170) and/or provide control signals to oneor more of throttling system 220, braking system 230, and steeringsystem 240 to navigate vehicle 200. In some embodiments, processing unit110 may continue to analyze images and/or radar output to determinewhether the system response should be interrupted (e.g., if the objecthas moved out of the path of vehicle 200, discontinue applying thebrakes).

FIGS. 10 through 13 represent various phases of the process foridentifying a target object region within one or more images. Forexample, FIG. 10 depicts a scene 1001 including a road 1002 on whichvehicle 200 is traveling. FIG. 10 may represent scene 1001 from, forexample, a forward facing perspective of vehicle 200. Consistent withthe discussion above, the true FOE for scene 1001 as vehicle 200 travelsalong road 1002 will appear to be located at point 1004. Positioned inone of the lanes of road 1002 is an upright hazard in the form of atrash can 1006. As vehicle 200 approaches trash can 1006, radar unit 801may acquire range and range rate information relative to vehicle 200 andtrash can 1006. From this information, a TTC between vehicle 200 andtrash can 1006 may be determined.

As vehicle 200 approaches trash can 1006, images of the scene, includingtrash can 1006, may be acquired by one or more of image acquisitiondevices 122, 124, and/or 126. As noted above, the radar information mayalso be used to identify regions of interest within captured imageswhere the detected target object may be located. FIGS. 11A and 11B showrepresentative regions of interest from each of a pair of acquiredimages, as determined based on the information associated with detectionby the radar unit 801 of trash can 1006. As vehicle 200 travels alongthe center of road 1002, a series of images may be captured showingtrash can 1006. Due to the motion of vehicle along road 1002 in thedirect of FOE 1004, images of trash can 1006 captured later in thesequence will show trash can 1006 expanding within the image frame aswell as moving down and to the right within the image frame (asrepresented by FIGS. 11A and 11B).

The optical flow from the image region shown in FIG. 11A to the imageregion in FIG. 11B may be determined. For example, based on any of themethods discussed above, the motion of any of selected target orreference points 1008, 1010, and 1012 (or any other number of target orreference points) on trash may 1006 may be determined from the frameshown in FIG. 11A to the frame shown in FIG. 11B. In some cases, thetarget or reference points may coincide with features in the imagedistinguishable from the immediate surroundings (e.g., a corner of anobject, small dark or light areas of an object, a light source, ahandle, bump, etc.). Such target or reference points may be readilyidentified in subsequent images and may provide a high level ofconfidence that the identified reference point in one or more subsequentimages corresponds with the reference point chosen from the originalimage. The determined motion constitutes the measured optical flow,which is a 2D vector, (u, v).

FIG. 12A provides a diagrammatic representation of plotted values of thehorizontal optical flow between FIGS. 11A and 11B. Similarly, FIG. 12Bprovides a diagrammatic representation of plotted values of the verticaloptical flow between FIGS. 11A and 11B. Using the radar range and radarrange rate information, an expected optical inflation magnitude may bedetermined as a scale factor. And, given the scale from the radar, allpixels, points, image segments, patches, etc. that share a common FOEmay be determined. The optical flow determination is separable, meaningthat it can be determined or solved separately for the measured verticalflow and the measured horizontal flow. Thus, for a givenradar-determined scale and vertical flow, for example, associated withvertical position (y) in the image of FIG. 12B, the following expressionmay be used:

FOE.y=y−V/(scale−1), where V is vertical flow and the scale is theoptical inflation magnitude derived from the radar range and range ratevalues.

Thus, applying this expression to the vertical flow data represented byFIG. 12B and scaling by the radar-determined scaling factor, a plotsimilar to the one shown in FIG. 13 may be obtained. FIG. 13 includes atarget object region 1301 coinciding with points in the image that, forthe measured radar range and radar rate values, share a common FOE.Target object region 1301 coincides with trash can 1006 and may be usedto confirm the presence of an actual target object as well as locate thetrash can spatially relative to vehicle 200 (using not only the radarrange and radar rate measurements, but also known characteristicsassociated with the image acquisition device(s) that captured the imagesof trash can 1006. As a result, edge locations, size and shapecharacteristics, etc. may all be determined for trash can 1006, and iftrash can 1006 resides in the path of vehicle 200, system 100 mayprovide a warning to a driver of vehicle 200 or cause one or morenavigational changes to occur.

The presently described embodiments may operate in a stand-alonecapacity in a navigational system for a vehicle. In some embodiments,though, the radar-cued navigational system may be integrated with oroperated in conjunction with one or more other navigational systems fora vehicle. For example, in some cases the radar-cued visual navigationsystem may operate with or be integrated with an advanced driver assistsystem (ADAS) and may be used to provide at least some functionality tothe ADAS system. Navigational assist modules for traffic signrecognition (TSR), lane departure warning (LDW), intelligent headlightcontrol (IHC), and/or traffic light detection may all benefit fromradar-cued output. For example, in some embodiments, as vehicle 200approaches an intersection, the optical flow of identified light sourcespresent in two or more images may be observed. The optical flow of suchlight sources may be compared to time to contact (TTC) values availablefrom radar unit 801 for stationary objects in the surroundingenvironment of vehicle 200 (such TTC values may be determined, forexample, based on radar range rate and vehicle speed). Where a TTC valuefor a stationary object agrees with the optical flow observed for alight source in the acquired images, application processor 180, e.g.,may determine that the light source is a traffic light or is a potentialtraffic light. If the light source is determined to be a traffic light,the system may further determine the color associated with the lightsource. If the light source is red or yellow, then a warning may beissued for the driver to slow to a stop. Alternatively, the system couldautomatically apply the brakes and slow the vehicle to a stop. If thelight source is green, then no navigational change may be deemednecessary.

The presently disclosed system may also operate according to differentmodes. For example, in some situations there may be benefits fromoperating the navigational system in a wide detect mode. For example, inan urban environment where vehicle speeds may be reduced, but the numberof potential target objects surrounding vehicle 200, such aspedestrians, lights, intersections, trash cans, etc., may be higher, thesystem may select a wide mode of operation. In other environments, suchas a rural road, for example, where speeds may be higher than in a city,and the number of potential target objects may be fewer, thenavigational system may select a narrow mode of operation. In someembodiments, the wide mode of operation may be associated with analysisof images from an image capture device having a wider field of view thananother available image capture device having a narrower field of view.The navigational system may select a certain camera (or lens wherelenses are selectable or adjustable) for image capture or image analysisbased on any suitable factor. For example, applications processor mayselect image acquisition device 122 for image capture or for imagestream analysis in certain situations and may select image acquisitiondevice 124 or 126 in other situations. In some embodiments, a camera,lens, or image stream may be selected based on radar range or range rateinformation obtained from radar unit 801, based on vehicle speed, basedon a recognized environment or target object density derived from thecaptured images and/or radar information, or using any other suitableparameter.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1. A navigation system for a vehicle, the system comprising: at leastone image capture device configured to acquire a plurality of images ofan environment; a radar sensor to detect an object in the environment;and at least one processing device programmed to: receive the pluralityof images from the at least one image capture device; receive outputfrom the radar sensor; determine, from the plurality of images, anindicator of optical flow; determine a value indicative of an expectedoptical inflation associated with the object in the plurality of images;determine from the plurality of images where the value indicative of anexpected optical inflation associated with the object substantiallymatches the indicator of optical flow; identify a target object region;and cause a system response based on the identified target objectregion.
 2. The navigation system of claim 1, wherein the target objectregion is associated with a common focus of expansion.
 3. The navigationsystem of claim 1, wherein the at least one processing device is furtherprogrammed to determine an angular position of the object relative tothe vehicle based on a determined location of the target object region.4. The navigation system of claim 1, wherein the at least one processingdevice is further programmed to determine a location of the objectrelative to the vehicle based on the identified target object region. 5.The navigation system of claim 1, wherein the system response includesat least one of braking or a change in current travel direction of thevehicle if the at least one processing device determines that thecurrent travel direction may result in a collision with the object. 6.The navigation system of claim 1, wherein the system response is anavigational response including braking.
 7. The navigation system ofclaim 1, wherein the system response is a navigational responseincluding a change in a direction of travel of the vehicle.
 8. Thenavigation system of claim 1, wherein the at least one processing deviceis further programmed to identify edges of the object based on theidentified target object region.
 9. The navigation system of claim 1,wherein the at least one processing device is further programmed todetermine a height of the object based on the identified target objectregion.
 10. The navigation system of claim 1, wherein the output of theradar sensor includes an indicator of a time to contact between thevehicle and the object.
 11. The navigation system of claim 1, whereinthe at least one processing device is configured to account for at leastone effect caused by use of a rolling shutter to acquire the pluralityof images in determining the indicator of optical flow.
 12. Thenavigation system of claim 1, wherein the at least one processing deviceis configured to select an operational mode from among at least a firstmode and a second mode, where the first mode is associated with a widerimage capture field of view than the second mode.
 13. A vehiclecomprising a navigation system, the navigation system comprising: atleast one image capture device configured to acquire a plurality ofimages of an environment; a radar sensor to detect an object in theenvironment; and at least one processing device programmed to: receivethe plurality of images from the at least one image capture device;receive output from the radar sensor; determine, from the plurality ofimages, an indicator of optical flow; determine a value indicative of anexpected optical inflation associated with the object in the pluralityof images; determine from the plurality of images where the valueindicative of an expected optical inflation associated with the objectsubstantially matches the indicator of optical flow; identify a targetobject region; and cause a system response based on the identifiedtarget object region.
 14. The vehicle of claim 13, wherein the at leastone processing device is further programmed to determine an angularposition of the object relative to the vehicle based on a determinedlocation of the target object region.
 15. The vehicle of claim 13,wherein the at least one processing device is further programmed todetermine a location of the object relative to the vehicle based on theidentified target object region.
 16. The vehicle of claim 13, whereinthe system response includes at least one of braking or a change incurrent travel direction of the vehicle if the at least one processingdevice determines that the current travel direction may result in acollision with the object.
 17. The vehicle of claim 13, wherein the atleast one processing device is further programmed to identify edges ofthe object based on the identified target object region.
 18. The vehicleof claim 13, wherein the at least one processing device is furtherprogrammed to determine a height of the object based on the identifiedtarget object region.
 19. The vehicle of claim 13, wherein the output ofthe radar sensor includes an indicator of a time to contact between thevehicle and the object.
 20. The vehicle of claim 13, wherein theprocessing device is further configured to identify a light source in atleast one of the plurality of images, determine whether the light sourceis a traffic light based on the indicator of optical flow and the outputof the radar sensor, and cause automatic application of brakes of thevehicle if the traffic light is determined to be yellow or red. 21-22.(canceled)